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Record W3031627807 · doi:10.1080/09687637.2020.1769560

Modes of cannabis use among Canadian youth in the COMPASS study; using LCA to examine patterns of smoking, vaping, and eating/drinking cannabis

2020· article· en· W3031627807 on OpenAlexafffundabout
Amanda Doggett, Kate Battista, Scott T. Leatherdale

Bibliographic record

VenueDrugs Education Prevention and Policy · 2020
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsCannabisEnvironmental healthCompassPsychologyDemographyMedicinePsychiatryGeographySociologyCartography

Abstract

fetched live from OpenAlex

Research has indicated that cannabis consumption through alternative modes of use such as eating/drinking or vaping may be increasing in areas where cannabis has been legalized. However, there is little research that examines these different modes of cannabis use in a Canadian youth context. The purpose of this study was to identify pre-legalization modes of cannabis use (smoking, eating/drinking, vaping) among a sample of 45,677 secondary school students who participated in year 6 (2017/18) of the COMPASS study. Within our sample, 24.9% reported cannabis use within the last 12 months; among those, 38.7% reported occasional use, and 61.3% reported current use. Multi-mode patterns of use were common; more than half of those reporting current cannabis use indicated a use pattern other than exclusively smoking, and over 20% reported use via all three modes. Findings from a latent class analysis identified three distinct groups and suggested that eating/drinking cannabis and vaping cannabis may more often be an addition to smoking rather than a replacement. Continued monitoring of these patterns is important for public health and necessary to evaluate any changes resulting from cannabis legalization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.353
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2020
Admission routes3
Has abstractyes

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